The Future of AI in Health & Life Sciences Education: Workshop

Theme: Integrating AI into Curriculum and Identifying Essential AI Education Components

Key Topics

• Integrating AI into Curriculum: Panelists will discuss how AI can be embedded in healthcare and life sciences education. This includes the types of AI skills needed for different roles, such as clinicians, researchers, and administrators.
• Essential AI Education Components: What are the critical AI and ML skills healthcare professionals need? This section covers foundational AI concepts, practical skills, and what should be taught at different levels (students, residents, practicing professionals).
  

Discussion Points:

• How are institutions currently integrating AI into their health sciences curriculum?
• What are the essential AI education components for healthcare professionals today?
• What gaps exist in current training programs, and how can they be addressed?

Facilitated Discussion: Attendees will break into small groups or engage in an open-floor discussion, sharing their thoughts on how AI education can be effectively implemented in healthcare settings.

• Key question: What barriers exist to integrating AI into the healthcare curriculum at different levels (undergrad, residency, practicing professionals)?

Theme: Teaching AI and ML to Trainees and Overcoming Barriers to Implementation

Key Topics:

• Teaching AI and ML to Trainees: This panel will explore the methodologies and modalities for teaching AI, including what works best for different groups (e.g., medical students vs. practicing clinicians).
• Effective Education Models: Panelists will share successful education models, including experiential learning and interdisciplinary approaches that have been effective in preparing students and professionals.
• Implementation Barriers: What are the obstacles to implementing AI education in medical institutions? Panelists will discuss challenges related to resources, faculty expertise, and institutional readiness.

Discussion Points:

• What are the most effective education models for teaching AI and ML in healthcare?
• How can experiential learning be incorporated to help learners apply AI concepts in real-world scenarios?
• What are the biggest barriers (technical, organizational, cultural) to teaching AI in medical education?
• How do different groups of learners (students, clinicians, researchers) prefer to learn about AI? What modalities (e.g. in-person workshops, online simulations, blended learning, etc) have been most effective / desirable?

Facilitated Discussion: Participants will dive deeper into how these education models can be implemented and the ways institutions can overcome the key barriers discussed.some text

• Key question: What practical steps can institutions take to introduce AI education at different levels, and what resources (funding, technology, faculty training) are needed?

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Summary and Next Steps: Facilitators will recap the day's key takeaways and outline potential action items. These might include drafting a follow-up article (as mentioned in the notes) and continuing to build a network of education and industry partners to advance AI literacy in healthcare.